Cell type annotation on GBM/HGG Xenium data
heinin
2025-10-11
Last updated: 2025-10-17
Checks: 6 1
Knit directory: 13384_GBMHGG_SPP1_Xenium/
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| absolute | relative |
|---|---|
| /home/hnatri/13384_GBMHGG_SPP1_Xenium/ | . |
| /home/hnatri/13384_GBMHGG_SPP1_Xenium/code/olors_themes.R | code/olors_themes.R |
| /home/hnatri/13384_GBMHGG_SPP1_Xenium/code/plot_functions.R | code/plot_functions.R |
| /home/hnatri/13384_GBMHGG_SPP1_Xenium/main_cluster_markers.tsv | main_cluster_markers.tsv |
| /home/hnatri/13384_GBMHGG_SPP1_Xenium/main_top_markers.tsv | main_top_markers.tsv |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b986e82 | heinin | 2025-10-16 | Spatial plots of marker expression |
Import packages
suppressPackageStartupMessages({
library(workflowr)
library(arrow)
library(Seurat)
library(SeuratObject)
library(SeuratDisk)
library(tidyverse)
library(tibble)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(googlesheets4)
library(workflowr)})Environment variables and helper functions
setwd("/home/hnatri/13384_GBMHGG_SPP1_Xenium/")
set.seed(9999)
options(scipen = 99999)
options(ggrepel.max.overlaps = Inf)
#source("/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/olors_themes.R")
source("/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/plot_functions.R")Import data
# Copied to isilon /tgen_labs/banovich/PIPAC/Seurat
seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/13384_GBMHGG_Xenium/Seurat/spatial_clustered_NN30_PC50_Seurat.rds")
# Removing UnassignedCodewords from the expression data
seurat_data <- subset(seurat_data, features = rownames(seurat_data)[-grep("UnassignedCodeword", rownames(seurat_data))])
seurat_data <- NormalizeData(seurat_data)
seurat_data <- ScaleData(seurat_data)
# saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/13384_GBMHGG_Xenium/Seurat/cell_clustered_NN30_PC50_Seurat.rds")
DimPlot(seurat_data,
group.by = "leiden_1.0",
#cols = main_cluster_col,
reduction = "umap",
raster = T,
label = T) +
coord_fixed(ratio = 1) +
theme_classic() +
NoLegend()
DimPlot(seurat_data,
reduction = "sp",
group.by = "leiden_1.0",
raster = T,
label = F) +
coord_fixed(ratio = 1) +
theme_minimal() +
NoLegend() +
ggtitle("TMA1_0069754")
Feature expression
grep("OLIG", rownames(seurat_data), value = T)[1] "OLIG1" "OLIG2"plot_features <- c("PTPRC",
"CD3D", "CD3E", "CD4", "CD8A", # T cells
"STAT4", "STAT3", "GZMB",
"SELL", "CD19", # B cells
"CD68", "CD44", "MARCO", "APOE", "C1QB", "SPP1",
"MS4A1", # Lineage markers
"FN1", "COL1A1", # Fibroblasts
"TP53", "KIT", # Tumor
"EPCAM",
"OLIG1", "OLIG2")
DotPlot(seurat_data,
group.by = "leiden_1.0",
features = plot_features,
cols = c("gray89", "tomato3")) +
RotatedAxis()
FeaturePlot(seurat_data,
slot = "data",
features = plot_features,
order = T,
ncol = 5,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
FeaturePlot(seurat_data,
slot = "data",
#split.by = "Sample",
features = plot_features,
order = T,
reduction = "sp",
raster = T,
ncol = 4,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
DenoIST
DefaultAssay(seurat_data) <- "denoist_RNA"
DotPlot(seurat_data,
group.by = "leiden_1.0",
features = plot_features,
cols = c("azure", "tomato3")) +
RotatedAxis()Top cluster markers
Idents(seurat_data) <- seurat_data$"leiden_1.0"
cluster_markers <- FindAllMarkers(seurat_data,
return.thresh = 0.01,
logfc.threshold = 0.5,
min.pct = 0.20,
verbose = T)
table(cluster_markers$cluster)
1 19 4 15 13 2 20 9 0 5 14 18 8 10 11 3 16 6 7 17 12
45 28 54 56 36 41 87 20 87 17 44 25 40 31 57 58 27 43 62 41 47 #hist(cluster_markers$avg_log2FC, main = "", xlab = "avg_log2FC", breaks = 100)
#hist(cluster_markers$p_val, main = "", xlab = "p_val", breaks = 100)
#hist(cluster_markers$p_val_adj, main = "", xlab = "p_val_adj", breaks = 100)
top_cluster_markers <- cluster_markers %>%
arrange(dplyr::desc(avg_log2FC)) %>%
group_by(cluster) %>%
dplyr::slice(1:10)create_dotplot_heatmap(seurat_object = seurat_data,
plot_features = unique(top_cluster_markers$gene),
group_var = "leiden_1.0",
group_colors = main_cluster_col,
column_title = "",
row_km = 5,
col_km = 5,
row.order = NULL,
col.order = NULL)Saving top markers and annotations
output_cluster_markers <- cluster_markers %>%
arrange(dplyr::desc(avg_log2FC)) %>%
group_by(cluster) %>%
dplyr::slice(1:30)
output_cluster_markers <- merge(top_cluster_markers, markers, by.x = "gene", by.y = "Gene")
write.table(output_cluster_markers, "/home/hnatri/13384_GBMHGG_SPP1_Xenium/main_cluster_markers.tsv",
quote = F, row.names = F, sep = "\t")
# Saving DenoIST top markers by original annotation
Idents(seurat_data) <- seurat_data$Annotation
cluster_markers <- FindAllMarkers(seurat_data,
return.thresh = 0.01,
logfc.threshold = 0.5,
min.pct = 0.20,
verbose = T)
output_cluster_markers <- cluster_markers %>%
arrange(dplyr::desc(avg_log2FC)) %>%
group_by(cluster) %>%
dplyr::slice(1:30)
output_cluster_markers <- merge(top_cluster_markers, markers, by.x = "gene", by.y = "Gene")
write.table(output_cluster_markers, "/home/hnatri/13384_GBMHGG_SPP1_Xenium/main_top_markers.tsv",
quote = F, row.names = F, sep = "\t")Subsetting immune and non-immune cells for subclustering
seurat_data$Lineage <- ifelse(seurat_data$leiden_0.5 %in% c(5, 6),
"Immune", "TumorStroma")
immune_subset <- subset(seurat_data, subset = Lineage == "Immune")
nonimmune_subset <- subset(seurat_data, subset = Lineage == "TumorStroma")
saveRDS(immune_subset, "/scratch/hnatri/13384_GBMHGG_SPP1_Xenium/cell_immune_subset.rds")
saveRDS(nonimmune_subset, "/scratch/hnatri/13384_GBMHGG_SPP1_Xenium/cell_nonimmune_subset.rds")
# To build on command line, run Rscript -e "rmarkdown::render('annotation_cell.Rmd')"
# Then "mv *.html /home/hnatri/13384_GBMHGG_SPP1_Xenium/docs/"
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.18.0 viridis_0.6.3 viridisLite_0.4.2
[4] RColorBrewer_1.1-3 googlesheets4_1.1.0 ggrepel_0.9.3
[7] ggpubr_0.6.0 lubridate_1.9.2 forcats_1.0.0
[10] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[13] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[16] ggplot2_3.4.2 tidyverse_2.0.0 SeuratDisk_0.0.0.9021
[19] Seurat_5.0.1 SeuratObject_5.0.1 sp_1.6-1
[22] arrow_21.0.0.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.20 splines_4.3.0 later_1.3.1
[4] cellranger_1.1.0 polyclip_1.10-4 fastDummies_1.7.3
[7] lifecycle_1.0.3 rstatix_0.7.2 doParallel_1.0.17
[10] rprojroot_2.0.3 globals_0.16.2 processx_3.8.1
[13] lattice_0.21-8 hdf5r_1.3.8 MASS_7.3-60
[16] backports_1.4.1 magrittr_2.0.3 limma_3.58.1
[19] plotly_4.10.2 sass_0.4.6 rmarkdown_2.22
[22] jquerylib_0.1.4 yaml_2.3.7 httpuv_1.6.11
[25] sctransform_0.4.1 spam_2.9-1 spatstat.sparse_3.0-1
[28] reticulate_1.29 cowplot_1.1.1 pbapply_1.7-0
[31] abind_1.4-5 Rtsne_0.16 presto_1.0.0
[34] BiocGenerics_0.48.1 git2r_0.32.0 circlize_0.4.15
[37] S4Vectors_0.40.2 IRanges_2.36.0 irlba_2.3.5.1
[40] listenv_0.9.0 spatstat.utils_3.0-3 goftest_1.2-3
[43] RSpectra_0.16-1 spatstat.random_3.1-5 fitdistrplus_1.1-11
[46] parallelly_1.36.0 leiden_0.4.3 codetools_0.2-19
[49] shape_1.4.6 tidyselect_1.2.0 farver_2.1.1
[52] stats4_4.3.0 matrixStats_1.0.0 spatstat.explore_3.2-1
[55] googledrive_2.1.0 jsonlite_1.8.5 GetoptLong_1.0.5
[58] ellipsis_0.3.2 progressr_0.13.0 ggridges_0.5.4
[61] survival_3.5-5 iterators_1.0.14 foreach_1.5.2
[64] tools_4.3.0 ica_1.0-3 Rcpp_1.0.10
[67] glue_1.6.2 gridExtra_2.3 xfun_0.39
[70] withr_2.5.0 fastmap_1.1.1 fansi_1.0.4
[73] callr_3.7.3 digest_0.6.31 timechange_0.2.0
[76] R6_2.5.1 mime_0.12 colorspace_2.1-0
[79] scattermore_1.2 tensor_1.5 spatstat.data_3.0-1
[82] utf8_1.2.3 generics_0.1.3 data.table_1.14.8
[85] httr_1.4.6 htmlwidgets_1.6.2 whisker_0.4.1
[88] uwot_0.1.14 pkgconfig_2.0.3 gtable_0.3.3
[91] lmtest_0.9-40 htmltools_0.5.5 carData_3.0-5
[94] dotCall64_1.0-2 clue_0.3-64 scales_1.2.1
[97] png_0.1-8 knitr_1.43 rstudioapi_0.14
[100] rjson_0.2.21 tzdb_0.4.0 reshape2_1.4.4
[103] nlme_3.1-162 GlobalOptions_0.1.2 cachem_1.0.8
[106] zoo_1.8-12 KernSmooth_2.23-21 parallel_4.3.0
[109] miniUI_0.1.1.1 pillar_1.9.0 vctrs_0.6.2
[112] RANN_2.6.1 promises_1.2.0.1 car_3.1-2
[115] xtable_1.8-4 cluster_2.1.4 evaluate_0.21
[118] cli_3.6.1 compiler_4.3.0 rlang_1.1.1
[121] crayon_1.5.2 future.apply_1.11.0 ggsignif_0.6.4
[124] labeling_0.4.2 ps_1.7.5 getPass_0.2-4
[127] plyr_1.8.8 fs_1.6.2 stringi_1.7.12
[130] deldir_1.0-9 assertthat_0.2.1 munsell_0.5.0
[133] lazyeval_0.2.2 spatstat.geom_3.2-1 Matrix_1.6-5
[136] RcppHNSW_0.5.0 hms_1.1.3 patchwork_1.1.2
[139] bit64_4.0.5 future_1.32.0 statmod_1.5.0
[142] shiny_1.7.4 highr_0.10 ROCR_1.0-11
[145] gargle_1.4.0 igraph_1.4.3 broom_1.0.4
[148] bslib_0.4.2 bit_4.0.5